In addition to visual effectiveness, we further compare the performance of these dehazing algorithms in detection and segmentation tasks using Foggy Cityscapes-DBF dataset. For object detection task, we adopt Faster-RCNN [1]} as our detector, which is pretrained on the Cityspaces dataset [2]}. We employ mAP and AP\(_{50}\)  to measure the detection effect. As seen in TABLE III, our algorithm achieves the highest detection precision in the dehazing results of nine haze concentrations. More intuitively, Fig. 8 shows that our method has lesser missed detection and is close to the target detection result under the haze-free scene compared other CNN-based methods. Meanwhile, we conduct similar experiments on the semantics segmentation task, whose metric consists of mIoU and mAcc. We exploit PSPNet [3]} as the semantics segmentation algorithm pretrained on the Cityspaces dataset. The comparison results are shown in TABLE IV. As revealed in Fig. 8, it can be oserved that our method leads to more consistent semantic information with ground-truth on the qualitative segmentation results. Note that conducting object detection and segmentation directly on hazy images and haze-free images are adopted as the baseline and ideal case, respectively.
